… using a smorgasbord of informative features significantly and consistently outperforms other
methods, even for translation tasks where PBSMT and NMT produce translations of a very …

… machine translation which we use to add a large number of features to a baseline system
exploiting features … Feature values were combined in a log-linear model to select the highest …

… This document describes the approach by the NLP Group at the Technical University of
Catalonia (UPC-LSI), for the shared task on Automatic Evaluation of Machine Translation at …

… the Moses Statistical Machine Translation (SMT) system default set of features using shallow
… default features, most systems are scalable and support any number of additional features. …

… This chapter concerns the use of corpora in Machine Translation (MT), and, to a lesser extent,
the … An essential feature is the availability of a suitable large bilingual corpus of reliable (…

… hierarchical statistical machine translation systems, two major features model translation:
rule translation probabilities and lexical smoothing scores. The rule translation probabilities are …

… Second, they automatically learn space invariant features. Spatial invariance is desirable in
vision since we often aim to recognize objects or features regardless of their exact position in …

… to machine translation in which large feature sets can be exploited. Unlike discriminative
reranking approaches, our system can take advantage of learned features … array of features and …

… translation and neural translation research. The evaluation task of machine translation is
not only to evaluate the quality of machine translation, but also to give timely feedback to …

In this paper we describe some experiments carried out to test the impact of automatic
casing and punctuation changes when training and testing statistical translation models. The …